Humans can often learn to distinguish previously unseen visual objects after being shown only a few example images of a novel object. Deep neural networks, on the other hand, typically require thousands of training images to generalize to new examples of classes seen during training.
The field of few-shot learning aims to address this by developing models that can work with scarce data: only a few examples being available for training. One-shot classification is a special case of few-shot learning, where the system needs to learn to classify after seeing only one example of each class. Although deep learning models have made great progress in few-shot learning [1]}, [2]}, [3]}, reaching human level performance has been hard [4]} even on simple datasets like Omniglot [5]} which is one the main benchmarks for this task.
